涉及或隐含距离计算的算法,比如K-means、KNN、PCA、SVM等,一般需要feature scaling, 损失函数中含有正则项时,一般需要feature scaling 梯度下降算法 特征工程-特征归一化 (Min-Max Scaling) 对原始数据进行线性变换,使结果映射到 [0,1] 的范围内,实现对原始数据的等比缩放。 归一化公式: 其中,X为原始数据,min...
数据归一化(Feature Scaling) 之前我们使用 kNN 算法来完成分类这个任务的时候,其实少做了非常重要的一步就是数据归一化(Feature Scaling)。 首先,我们来看看,为什么我们需要做数据归一化?我依然举出肿瘤这个例子,假设我们有两个特征,一个是肿瘤大小(单位是cm),一个是发现天数(单位是天)。样本1中肿...
涉及或隐含距离计算的算法,比如K-means、KNN、PCA、SVM等,一般需要feature scaling,因为: zero-mean一般可以增加样本间余弦距离或者内积结果的差异,区分力更强,假设数据集集中分布在第一象限遥远的右上角,将其平移到原点处,可以想象样本间余弦距离的差异被放大了。在模版匹配中,zero-mean可以明显提高响应结果的区分度。
Please note that not all ML algorithms will require feature scaling. The general rule of thumb is that algorithms that exploit distances or similarities between data samples, such as artificial neural network (ANN), KNN, support vector machine (SVM), and k-means clustering, are sensitive to ...
In our experiment, the performance of the proposed algorithm is better than RADAR, FS-kNN and CFS-kNN, in term of accuracy. Moreover, ANN is more flexible and reasonable than other fixed structure schemes to determine the weights used in the feature-scaling based algorithms. The algorithm is...
linear-regression logistic-regression knn decision-tree-classifier classification-algorithm dbscan-clustering arima-model decision-tree-regression agglomerative-clustering dbscan-clustering-algorithm elasticnetregression randomized-search-cross-validation featureselection elasticnet-regression ploynomialregression handling-...
Furthermore, the authors introduce a KNN graph construction method that uses pooling for fuzzy distance computation. Their experimental results yield accuracy scores greater than 99% even with very few labels. Recently, Wang et al. [72] proposed a fault estimation technique in conjunction with a ...
The final classification on the optimal feature set is done by using K-nearest neighbor (KNN) classifier, thus achieving commendable results on the three publicly available HAR datasets that have been used to evaluate the proposed sensor-based HAR model. Literature analysis HAR is one the important...
number and size of branches in a decision learning system, to estimate the relative importance of each attribute. Using the information gain method, the authors constructed a feature weighted matrix of nine technical in- dicators, which were inputs in the SVM and k-nearest neighbor (KNN) ...
Beginning with a preprocessing stage, where the majority of applicable features are chosen by the correlation matrix, then three data analytics methods such as (neural networks, SVM, and KNN) on data sets of different dimensions are applied, to detect the accuracy and ability of all of the ...